The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

We proposed a Deep Self-Organizing Map (DSOM) algorithm which is completely different from the existing multi-layers SOM algorithms, such as SOINN. It consists of layers of alternating self-organizing map and sampling operator. The self-organizing layer is made up of certain numbers of SOMs, with each map only looking at a local region block on its input. The winning neuron's index value from every SOM in self-organizing layer is then organized in the sampling layer to generate another 2D map, which could then be fed to a second self-organizing layer. In this way, local information is gathered together, forming more global information in higher layers. The construction method of the DSOM is unique and will be introduced in this paper. Experiments were carried out to discuss how the DSOM architecture parameters affect the performance. We evaluate our proposed DSOM on MNIST and CASIA-HWDB1.1 dataset. Experimental results show that DSOM outperforms the original supervised SOM by 7:17% on MNIST and 7:25% on CASIA-HWDB1.1.

Deep Self-Organizing Map for visual classification

In the field of Image compression and particularly for the process of image encoding, Fractal image compression (FIC) technique plays a vital role. This technique is based on fractals present in an image and also it is capable of generating the copying blocks dependent on numerical changes. The only drawback in the Fractal image compression is that the time taken during the encoding process when the data is large. Thus, it is necessary to optimize the encoding process for efficient resource utilization as optimization algorithms are generally known for converging behavior. This research work attempts to apply the Tree Seed Algorithm as a characteristic enlivened high-level algorithm to improve FIC. Output shows the improvement of such calculation under various types (encoding time, pressure proportion, top sign to clamor proportion, and mean square error. Moreover, an examination with a portion of the current techniques underlines this addition.

Enhanced Image Compression Using Fractal and Tree Seed-Bio Inspired Algorithm

Application of Improved HMM Algorithm in Slag Detection System

The growing neural gas (GNG) self-organizing neural network stands as one of the most successful examples of unsupervised learning of a graph of processing units. Despite its success, little attention has been devoted to its extension to a hierarchical model, unlike other models such as the self-organizing map, which has many hierarchical versions. Here, a hierarchical GNG is presented, which is designed to learn a tree of graphs. Moreover, the original GNG algorithm is improved by a distinction between a growth phase where more units are added until no significant improvement in the quantization error is obtained, and a convergence phase where no unit creation is allowed. This means that a principled mechanism is established to control the growth of the structure. Experiments are reported, which demonstrate the self-organization and hierarchy learning abilities of our approach and its performance for vector quantization applications.

The Growing Hierarchical Neural Gas Self-Organizing Neural Network

The increase of the need for image storage and transmission in computer systems has increased the importance of signal and image compression algorithms. The approach involving vector quantization (VQ) relies on the design of a finite set of codes which will substitute the original signal during transmission with a minimal of distortion, taking advantage of the spatial redundancy of image to compress them. Algorithms such as LBG and SOM work in an unsupervised way toward finding a good codebook for a given training data. However, the number of code vectors (N) needed for VQ increases with the vector dimension, and full-search algorithms such as LBG and SOM can lead to large training and coding times. An alternative for reducing the computational complexity is the use of a tree-structured vector quantization algorithm. This paper presents an application of a hierarchical SOM for image compression which reduces the search complexity from O(N) to O(log N), enabling a faster training and image coding. Results are given for conventional SOM, LBG and HSOM, showing the advantage of the proposed method.

D. Neto

Papers

Hierarchical SOM applied to image compression